Objectives: The relationship between plasma low-density lipoprotein (LDL) cholesterol and the risk of coronary artery disease (CAD) is known, but the other characteristics of LDL, particularly particle size and density, are unclear. The relationship between small dense LDL phenotype and non-diabetic, normocholesterolemic CAD was investigated in 70 patients with angiographically documented CAD, and 38 age-matched control subjects.
Methods: Peak LDL particle diameter was determined by using 2-16% polyacrylamide gradient gel electrophoresis. Small dense LDL phenotype was defined as particle diameter equal to or less than 255 A.
Results: LDL particle diameters in patients with CAD were significantly smaller than those in controls (252.4 +/- 6.9 vs 259.3 +/- 8.8 A, mean +/- SD, p < 0.0001). Prevalence of small dense LDL was markedly higher in patients with CAD (72%) than in subjects without CAD (24%). CAD patients had significantly lower high-density lipoprotein (HDL)-cholesterol and apolipoprotein A-I levels (39.3 +/- 8.8 vs 49.8 +/- 12.0, 108.1 +/- 20.6 vs 122.9 +/- 20.1 mg/dl), and higher lipoprotein (a) and apolipoprotein B levels (28.8 +/- 30.4 vs 16.8 +/- 18.8, 96.5 +/- 21.8 vs 80.2 +/- 14.9 mg/dl) than non-CAD subjects, whereas total cholesterol, LDL-cholesterol, triglyceride, remnant-like particle cholesterol and insulin levels were not increased in CAD patients compared with non-CAD subjects. Stepwise regression analysis revealed that LDL particle size was the most powerful independent determinant of CAD (F value = 20.04, p < 0.0001). Logistic regression analysis revealed that small dense LDL phenotype [relative risk (RR) of 7.0, 95% confidence interval (95% CI) 2.4-20.1], low HDL-cholesterol (RR of 5.6, 95% CI 2.1-15.2), and increased apolipoprotein B (RR of 5.8, 95% CI 1.8-18.5) were independently associated with incidence of CAD.
Conclusions: High prevalence of small dense LDL is a leading cause of CAD with even normal cholesterol levels.
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Sci Rep
January 2025
School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, Belgrade, Serbia.
The expansion of LEAN and small batch manufacturing demands flexible automated workstations capable of switching between sorting various wastes over time. To address this challenge, our study is focused on assessing the ability of the Segment Anything Model (SAM) family of deep learning architectures to separate highly variable objects during robotic waste sorting. The proposed two-step procedure for generic versatile visual waste sorting is based on the SAM architectures (original SAM, FastSAM, MobileSAMv2, and EfficientSAM) for waste object extraction from raw images, and the use of classification architecture (MobileNetV2, VGG19, Dense-Net, Squeeze-Net, ResNet, and Inception-v3) for accurate waste sorting.
View Article and Find Full Text PDFPLoS One
January 2025
Peking University Health Science Center, Macao Polytechnic University Nursing Academy, Macao Polytechnic University, Macao, China.
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Angew Chem Int Ed Engl
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Beihang University, 37 Xue Yuan Road, Hai Dian District, 100191, Beijing, CHINA.
Sodium-ion batteries (SIBs), endowed with relatively small Stokes radius and low desolvation energy for Na+, are reckoned as a promising candidate for fast-charging endeavors. However, the C-rate charging capability of practical energy-dense sodium-ion pouch cells is currently limited to ≤1C, due to the high propensity for detrimental metallic Na plating on the hard carbon (HC) anode at elevated rates. Here, an ampere-hour-level sodium-ion pouch cell capable of 3C charging is successfully developed via phosphorus (P)-sulfur (S) interphase chemistry.
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January 2025
Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.
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View Article and Find Full Text PDFPlant Dis
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Kashi, Xinjiang, China, China;
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